2 Methods

2.1 Introduction

This chapter provides details on the methods used in the
production of the report, including details about the data and the
analytical approach. First, it provides details about the Growing
Up in Scotland (
GUS) data,
including detailed notes about the sample selected for the analysis
presented here. Next, it outlines the analytical approach taken,
including notes on how to interpret the findings.

2.2 Data and sample

The analysis presented in this report uses data from the two
GUS birth
cohorts. More specifically it uses data collected at the three age
points where comparable data is available, namely when the cohort
children were aged 10 months, 3 years and 5 years
[9]. For Birth Cohort 1 (
BC1), this means the
analysis draws on data collected in 2005/06, 2007/08 and 2009/10.
For Birth Cohort 2 (
BC2) the corresponding data
were collected in 2011, 2013 and 2015 (Table 2‑1).

Table 2‑1: Sample overview

Child’s age

10 months

3 years

5 years

Cohort

BC1

BC2

BC1

BC2

BC1

BC2

Year of data collection

2005/06

2011

2007/08

2013

2009/10

2015

Number of mothers

5147

6007

4131

4874

3759

4283

The main data collection on
GUS takes place
through annual or biennial ‘sweeps’ of face-to-face
survey interviews with the cohort child’s main carer. In the
vast majority of cases this is the child’s mother. At each
sweep, information previously collected about the mother’s
employment is checked and, where necessary, updated. If the mother
has a resident partner their employment details are collected too.
In addition to this, a range of information about the household is
obtained including household income and the mother’s
educational attainment.

This means that
GUS data contains
information about mothers’ employment as well as about a
range of individual and household circumstances collected across a
number of different time points.

Given the focus of the research, cases where information about
the mother's employment status was missing at one or more relevant
sweeps were excluded from the analysis, and all analyses were
restricted to cases where the main respondent was the cohort
child’s mother
[10].

Analysis which used information about the mother’s
employment during pregnancy was further restricted to cases where
the respondent was the child’s biological mother.
Furthermore, some analyses use data only from the youngest cohort (
BC2). Finally, for the
longitudinal analyses only cases where the cohort child’s
mother was the main respondent at all relevant sweeps were
included.

These restrictions mean that base sizes vary across and within
chapters. Clear descriptions of the groups included in the analysis
are provided in the text and base sizes are provided for all tables
and charts.

2.3 Analytical approach and interpreting the
findings

The report makes comparisons between mothers in the two cohorts,
as well as comparisons between mothers within each cohort at
different time points: at the time the cohort child was aged 10
months, 3 years and 5 years. It also looks at mothers’
employment status and certain employment trajectories according to
a number of socio-economic and demographic characteristics. Details
of key variables are provided at the beginning of the relevant
chapters. Details of the remaining variables are available in
Appendix A[11].

As already noted, the
GUS sample design
means that the data can be used to produce estimates about all
mothers of children of a certain age living in Scotland. For
example, based on
GUS data we can
estimate the proportion of mothers of 5 year old children living in
Scotland in 2009/10 and in 2015 who were in paid work.

A substantial part of the analysis presented in this report
consists of bivariate analyses comparing differences in outcomes or
experiences for mothers according to their status measured using a
single variable – for example, employment status or household
income. Unless otherwise stated, only differences which were
statistically significant at the 95% level or above are commented
on in the text.

Not all families who initially took part in
GUS did so for
all of the subsequent sweeps. There are a number of reasons why
respondents drop out from longitudinal surveys and such attrition
is not random. Therefore, the data were weighted using specifically
designed weights which adjust for non-response and sample
selection. Different weights were applied for cross-sectional and
longitudinal analyses. All results have been calculated using
weighted data and all comparisons take into account the complex
clustered and stratified sample structures.

2.3.1 Multivariable analysis

Many of the factors we are interested in are related to each
other as well as being related to maternal employment. For example,
younger mothers are more likely to have lower educational
qualifications, to be lone parents, and to live in areas with high
levels of deprivation. Simple analysis may identify a relationship
between maternal age and maternal employment – for example,
younger mothers are more likely to give up work after having a
child. However, this relationship may be occurring because of the
underlying association between maternal age and education. Thus, it
may actually be the lower education levels among younger mothers
which are driving the association with giving up work rather than
the fact that they are younger in age.

To avoid this difficulty, multivariable regression analysis was
used. This analysis allows the examination of the relationships
between an outcome variable (e.g. whether a mother gave up work
after having a child) and multiple explanatory variables (e.g. the
mother’s age and education level, household income, whether
she lived with a partner, etc.) whilst controlling for the
interrelationships between each of the explanatory variables. This
means it is possible to identify an independent relationship
between any single explanatory variable and the outcome variable.
In this report, this means, for example, that we can identify
characteristics which are independently associated with being in
the position of seeking work whilst having a young child, and
characteristics independently associated with giving up work after
having a child. Note, though, that the identification of
associations between one or more explanatory variables and an
outcome variable does not necessarily imply that the explanatory
variable(s)
causes the outcome.

The multivariable analysis undertaken for this report uses
logistic regression models. Full results of the models are included
in the
Technical Annex along with notes on how to
interpret them.

Note that the statistical analysis and approach used in this
report represents one of many available techniques capable of
exploring this data. Other analytical approaches may produce
different results from those reported here.